Today is the anniversary for Serendipity35 which begins its thirteenth year of in existence. It's easy to remember because it is also Groundhog Day here in the U.S.

This is a rather silly celebration that is based on the notion that if the groundhog sees its shadow as it comes out of its den today, we have six weeks of winter to go. If the day is cloudy and the groundhog sees no shadow, it is a sign of coming spring and so it stays above ground. Why a cloudy day would signal an early spring and a sunny day would mean more winter has never made any sense to me.

If there is any fact or even some science to this silly day, it would be in the past. The ancient peoples and our own Native Americans knew that the behavior of animals, insects, and plants could help predict the weather. They took that to mean that these things might also be indicators of the coming and going of seasons.

Consider it weather lore, but there are lots of ideas about how to predict the severity of winter based on natural indicators. If I want to make some leap to education here, I guess I would have to say that we do look to trends outside education (business, technology, media etc.) as ways to predict where education might be headed. I'm working on a piece for next week today about how corporate professional learning is pulling educators into top roles at companies like Amazon. Will that affect higher education?

My Groundhog Day tradition has become to watch the film, Groundhog Day, which I have seen many times (which is actually pretty meta if you know what the film is about). I believe that the film is a whole lot more than just a comedy, and I am not alone in that opinion. Connect the movie to education? Well, I have seen in my 40 years in education a lot of repeating of things with little changes in the hopes of making things better - a theme of the film. Most of the time it results in minor improvements, sometimes in worst results, but we keep trying new approaches. Sometimes we see sunshine or clouds and think it will indicate what is to come. It is a 50/50 possibility, so why not predict.

I do know that the vernal equinox will arrive on time, but even that may or may not mean that springlike weather will arrive. And I do know that the spring semester will end on time and a new one will begin whether or not we see a shadow.

HBO's Westworld, which both creates fear of singularity and points to some multiplicity

Have you heard Stephen Hawking and Elon Musk raising concerns about AI and the singularity? These are fears that others have voiced for many decades and that have filled science-fiction stories for even longer. Singularity is the term given to that point when machines will surpass us.

That point will arrive, though no predictions have so far been correct on when it will occur. A more reasonable approach seem to me to be what some have called the "multiplicity." That is a way of viewing what is coming as a time of humans working more closely with machines rather than humans versus the machines.

An article in Wired quotes C Berkeley roboticist Ken Goldberg as saying that the multiplicity is "something that's happening right now, and it's the idea of humans and machines working together.”

I know all the automotive buzz is about driverless cars, but today in my car algorithms are guiding me to my destination, reminding me to stay in my lane, gently applying the brakes and steering when I am less attentive than I should be. My new car seems to be constantly flashing and beeping about something. I fear that the more it does, the more it distracts me from driving. Okay, maybe not that bad.

It is one thing to put your learning into the virtual hands of algorithms, but I am already entrusting a bit of my life protection in the car to them.

The multiplicity concept is not that new. A talk at Davos in 2015 points out that though there are now over a million robots working in factories around the world, we still don’t have them in our homes.

Hans Moravec pointed out 3 decades ago that “Tasks that are hard for humans, like precision spot welding, are easy for robots, while tasks that are easy for humans, like clearing the dinner table, are very hard for robots.”

The hospital robot that delivers drugs and linens to nurses and the ones in warehouses rolling 24/7 through the aisles scanning inventory or puling out items for orders hasn't necessarily surpassed humans in intelligence. But it is willing to work all day and night without breaks or pay. Do all robots replace humans? Much research says no, that they are more likely to enhance human workers or change what humans will do.

But the fear of the singularity remains.

Amazon's fulfillment centers use around 100,000 robots to bring products to people who are still better at packing them for shipping. Those clever robots still have trouble with simple human tasks like picking up things with their end effectors (hands).

The word multiplicity actually makes me think of a comedy film with Michael Keaton. In that Multiplicity, an overly busy human is able to clone himself multiple times in order to get done all the things he wants to do and still have time to live a life with his family.

An update of that 1996 film would probably change cloning to robots.

And that has really been the ultimate goal with AI and robots - to empower humans, not replace them. But the job-killing robot scenario is a tough one to dispel and you can find examples of jobs that disappear because of automation. San Francisco is supposedly considering a tax on robots that replace human workers.

Long before robots, automation threatened and replaced some human labor. The transition to common robot and AI use in our lives will likely be more gradual.

Yes, Westworld is scary, both in how the robots interact with humans, and in how the humans treat the robots.

When the singularity does arrive, make sure you know how to power down that robot.

In microeconomics and management, going vertical or vertical integration occurs when the supply chain of a company is owned by that company. For example, if a car manufacturer also produces its own steel, tires and batteries.

This is in contrast with horizontal integration, wherein a company produces several items which are related to one another.

Higher education has been a vertical enterprise for centuries. We keep knowledge creation, teaching, testing, and credentialing all under one company/college banner.

These are terms from economics and business. Are they applicable to discussions about education?

Horizontal integration often occurs in the business world by internal expansion, acquisition or merger. Of course, that might happen in education too, but there are also signs that it is happening in other ways.

In more general terms, assessment alignment is often the reason for both horizontal and vertical alignment in education. Alignment is typically understood as the agreement between a set of content standards and an assessment used to measure those standards. By establishing content standards, stakeholders in an education system determine what students are expected to know and be able to do at each grade level.

Probably, it is best when education goes both vertically and horizontally.

Horizontal information exchange can be teachers sharing methodology, students sharing information, students helping each other learn.

When a curriculum is truly vertically aligned or vertically coherent, what students learn in one lesson, course, or grade level prepares them for the next lesson, course, or grade level. I know teaching is supposed to be structured and logically sequenced so that learning progressively prepares them for more challenging, higher-level work. I saw that structured sequencing more in my K-12 teaching than I do in higher education which is more siloed.

I first encountered a chatterbot, it was ELIZA on the Tandy/Radio Shack computers that were in the first computer lab in the junior high school where I taught in the 1970s.

ELIZA is an early natural language processing program that came into being in the mid-1960s at the MIT Artificial Intelligence Laboratory. The original was by Joseph Weizenbaum, but there are many variations on it.

This was very early artificial intelligence. ELIZA is still out there, but I have seen a little spike in interest because she was featured in an episode of the TV show Young Sheldon. The episode, "A Computer, a Plastic Pony, and a Case of Beer," may still be available at www.cbs.com. Sheldon and his family become quite enamored by ELIZA, though the precocious Sheldon quickly realizes it is a very limited program.

ELIZA was created to demonstrate how superficial human to computer communications was at that time, but that didn't mean that when it was put on personal computers, humans didn't find it engaging. Sure, kids had fun trying to trick it or cursing at it, but after awhile you gave up when it started repeating responses.

The program in all the various forms I have seen it still uses pattern matching and substitution methodology. She (as people often personified ELIZA), gives canned responses based on a keyword you input. If you say "Hello," she has a ready response. If you say "friend," she has several ways to respond depending on what other words you used. Early users felt they were talking to "someone" who understood their input.

ELIZA was one of the first chatterbots (later clipped to chatbot) and a sample for the Turing Test. That test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human, is not one ELIZA can pass by today's standards. ELIZA fails very quickly if you ask her a few complex questions.

The program is limited by the scripts that are in the code. The more responses you gave her, the more variety there will be in her answers and responses. ELIZA was originally written in MAD-Slip, but modern ones are often in JavaScript or other languages. Many variations on the original scripts were made as amateur coders played around with the fairly simple code.

One variation was called DOCTOR and was made to be a crude Rogerian psychotherapist who likes to "reflect" on your questions by turning the questions back at the patient. This was the version that my students when I taught middle school found fascinating and my little programming club decided to hack the code and make their own versions.